Real-Time Prediction Model of Carbon Content in RH Process

Abstract

In the Ruhrstahl–Heraeus (RH) vacuum degassing process, we propose a real-time prediction model for the carbon content in molten steel, and show that the decarburization endpoint can be accurately determined using this model. Firstly, we applied a novel off-gas analyzer that can measure the carbon oxide concentration produced in the decarburization reaction faster and more accurately. Next, we generate decarburization curves using the off-gas components measured by the new analyzer. The decarburization curve describes the carbon content profile well during operation, and shows good agreement with the actual carbon content. In order to predict the carbon content during operation in real time, we create an artificial neural network (ANN) using the decarburization curves and operation data. By comparing the endpoint carbon content measured at the end of the operation with the predicted values, we confirmed the excellent predictive performance of the ANN model. Finally, we show that it is possible to accurately determine the decarburization endpoint using the prediction model. We expect that the proposed real-time prediction model can increase the productivity of the RH process.